Kernel: Anaconda (Python 3)
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import numpy as np import matplotlib.pyplot as plt %matplotlib notebook plt.style.use('ggplot') import random from random import randint import math
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food_amount = 100 pacman_amount = 20 board_size = 1000 default_health = 5 pacmen = [] food = [] crot = 1 cloc = 0 cbite = 1 turn_loss = 0 phi_prec = (80/255.) alpha_prec = 10/255. delta_prec = 10/255. pacman_radius = 1 food_health = 5
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class pacman: def __init__(self): #change dynamically self.x = randint(0,board_size) self.y = randint(0,board_size) self.theta = randint(0,359) self.health = default_health self.active = 1 #dna self.phi = randint(0,255) self.alpha = randint(0,255) self.delta = randint(0,255) self.phi_val = self.phi*phi_prec self.alpha_val = self.alpha*alpha_prec self.delta_val = self.delta*delta_prec #self.f = closest food #self.dist = closest food distance def __bool__(self): return True def __repr__(self): return "{pacman, x:"+str(self.x)+", y:"+str(self.y)+", theta:"+str(self.theta)+"}" def find_dist(self,f): return np.sqrt((self.x-f.x)**2 + (self.y-f.y)**2) def find_angle(self,f): dx = f.x - self.x dy = f.y - self.y arc = math.degrees(math.atan((dy/dx))) theta1 = 1 if dx > 0: if dy > 0: theta1 = 180 - arc else: theta1 = 180 + arc else: if dy > 0: theta1 = arc else: theta1 = 360 - arc if theta1 < 0: theta1 = 360 + theta1 #print(theta1) return theta1 def check_angle(self,f):#check if the food is in the range left_bound = self.theta - (self.phi_val/2) right_bound = self.theta + (self.phi_val/2) f_angle = self.find_angle(f) if(left_bound < f_angle and right_bound > f_angle): return True if(left_bound < f_angle + 360. and right_bound > f_angle + 360.): return True return False def find_food(self): global food closest = False closest_dist = False for i in food: dist = self.find_dist(i) if((dist < closest_dist or not closest_dist) and (self.check_angle(i))): closest = i closest_dist = dist self.f = closest self.dist = closest_dist if(closest != False): return True return False def rotate_to_food(self): #cost of rotate = crot*theta*health #no cost to rotation new_theta = self.find_angle(self.f) error = random.uniform(self.alpha_val/-2., self.alpha_val/2.) self.theta = new_theta + error def get_food(self): self.rotate_to_food() error = random.uniform(self.delta_val/-2., self.delta_val/2.) new_dist = self.dist + error angle = self.find_angle(self.f) self.x += new_dist*np.cos(angle) self.y += new_dist*np.sin(angle) cost = cloc*new_dist self.health -= cost #cost of rotate def bite(self): to_food = self.find_dist(self.f) self.health -= cbite if(to_food < pacman_radius):#means the bite was sucessful self.health += food_health self.f.reset() def random_rotate(self): self.theta = randint(0,359) #self.health += randint(-2,1) def step(self): self.health -= turn_loss if(self.find_food()): self.get_food() self.bite() self.random_rotate() def get_genes(self): return[self.phi,self.alpha,self.delta] def set_genes(self,genes): self.phi = genes[0] self.alpha = genes[1] self.delta = genes[2] def to_bits(self): genes = self.get_genes() split_genes = [] for i in genes: bin_str = format(i, '#010b') bin_str = bin_str[2:] split_genes.append([bin_str[:4],bin_str[4:]]) return split_genes def reproduce(self,other): son1 = pacman() son2 = pacman() son1_genes = [] son2_genes = [] mybits = self.to_bits() otherbits = other.to_bits() for i,v in enumerate(mybits): son1_gene = v[0]+otherbits[i][1] son2_gene = otherbits[i][0]+v[1] son1_gene = int('0b'+son1_gene,base=2) son2_gene = int('0b'+son2_gene,base=2) son1_genes.append(son1_gene) son2_genes.append(son2_gene) son1.set_genes(son1_genes) son2.set_genes(son2_genes) return [son1,son2] p = pacman() p2 = pacman() print(p.reproduce(p2))
[{pacman, x:445, y:858, theta:300}, {pacman, x:108, y:770, theta:101}]
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class dot: def __init__(self): self.x = random.uniform(0, board_size) self.y = random.uniform(0, board_size) self.active = 1 def __repr__(self): return "{dot, x:"+str(self.x)+", y:"+str(self.y)+"}" def reset(self): self.x = random.uniform(0, board_size) self.y = random.uniform(0, board_size)
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def gen1(): global pacmen pacmen = [] for i in range(0,pacman_amount): p = pacman() pacmen.append(p) pacmen = np.array(pacmen) print(pacmen[0]) return pacmen def food1(): global food food = [] for i in range(0,food_amount): f = dot() food.append(f) food = np.array(food) return food
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def driver(gen,life_span,generations): food = food1() dt = 1 t = [0] tracker = [] genetics = [] global pacmen for g in range(0,generations): #iterates through each generation tracker.append([]) genetics.append(pacmen[1].phi) for i in pacmen: tracker[g].append([]) t.append(t[-1]+dt) for q in range(0,life_span): #runs through time with current generation for i,v in enumerate(pacmen): tracker[g][i].append([v.x,v.y,v.theta]) v.step() #makes the next generation sons = [] healthy_pacmen = [] for i,v in enumerate(pacmen): #end of a generation mean reproduce if(v.health > 0 or True): #will never kill off healthy_pacmen.append(v) for i,v in enumerate(healthy_pacmen): if( i % 2 == 1): temp = v.reproduce(healthy_pacmen[randint(0,len(healthy_pacmen)-1)]) sons.extend(temp) #print('generation over') pacmen = sons return t,tracker,genetics
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t,tracker,genetics = driver(gen1(),10,20)
{pacman, x:771, y:154, theta:235}
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#print(tracker[0]) print(genetics) plt.figure() plt.plot(genetics)
[32, 96, 0, 16, 0, 176, 32, 96, 160, 96, 160, 96, 96, 96, 160, 160, 0, 0, 160, 160]
<IPython.core.display.Javascript object>
[<matplotlib.lines.Line2D at 0x7f57671c8ba8>]
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